Below is a selection of dissertations from the Doctor of Philosophy in Computational and Data Sciences program in Schmid College that have been included in Chapman University Digital Commons. Additional dissertations from years prior to 2019 are available through the Leatherby Libraries' print collection or in Proquest's Dissertations and Theses database.

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Dissertations from 2023

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Computational Analysis of Antibody Binding Mechanisms to the Omicron RBD of SARS-CoV-2 Spike Protein: Identification of Epitopes and Hotspots for Developing Effective Therapeutic Strategies, Mohammed Alshahrani

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Integration of Computer Algebra Systems and Machine Learning in the Authoring of the SANYMS Intelligent Tutoring System, Sam Ford

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Voluntary Action and Conscious Intention, Jake Gavenas

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Computational Modeling of Superconductivity from the Set of Time-Dependent Ginzburg-Landau Equations for Advancements in Theory and Applications, Iris Mowgood

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Application of Machine Learning Algorithms for Elucidation of Biological Networks from Time Series Gene Expression Data, Krupa Nagori

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Stochastic Processes and Multi-Resolution Analysis: A Trigonometric Moment Problem Approach and an Analysis of the Expenditure Trends for Diabetic Patients, Isaac Nwi-Mozu

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Causal Inference and Machine Learning Methods in Parkinson's Disease Data Analysis, Albert Pierce

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Causal Inference Methods for Estimation of Survival and General Health Status Measures of Alzheimer’s Disease Patients, Ehsan Yaghmaei

Dissertations from 2022

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Computational Approaches to Facilitate Automated Interchange between Music and Art, Rao Hamza Ali

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Causal Inference in Psychology and Neuroscience: From Association to Causation, Dehua Liang

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Advances in NLP Algorithms on Unstructured Medical Notes Data and Approaches to Handling Class Imbalance Issues, Hanna Lu

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Novel Techniques for Quantifying Secondhand Smoke Diffusion into Children's Bedroom, Sunil Ramchandani

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Probing the Boundaries of Human Agency, Sook Mun Wong

Dissertations from 2021

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Predicting Eye Movement and Fixation Patterns on Scenic Images Using Machine Learning for Children with Autism Spectrum Disorder, Raymond Anden

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Forecasting the Prices of Cryptocurrencies using a Novel Parameter Optimization of VARIMA Models, Alexander Barrett

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Applications of Machine Learning to Facilitate Software Engineering and Scientific Computing, Natalie Best

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Exploring Behaviors of Software Developers and Their Code Through Computational and Statistical Methods, Elia Eiroa Lledo

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Assessing the Re-Identification Risk in ECG Datasets and an Application of Privacy Preserving Techniques in ECG Analysis, Arin Ghazarian

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Multi-Modal Data Fusion, Image Segmentation, and Object Identification using Unsupervised Machine Learning: Conception, Validation, Applications, and a Basis for Multi-Modal Object Detection and Tracking, Nicholas LaHaye

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Machine-Learning-Based Approach to Decoding Physiological and Neural Signals, Elnaz Lashgari

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Learning-Based Modeling of Weather and Climate Events Related To El Niño Phenomenon via Differentiable Programming and Empirical Decompositions, Justin Le

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Quantum State Estimation and Tracking for Superconducting Processors Using Machine Learning, Shiva Lotfallahzadeh Barzili

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Novel Applications of Statistical and Machine Learning Methods to Analyze Trial-Level Data from Cognitive Measures, Chelsea Parlett

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Optimal Analytical Methods for High Accuracy Cardiac Disease Classification and Treatment Based on ECG Data, Jianwei Zheng

Dissertations from 2020

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Development of Integrated Machine Learning and Data Science Approaches for the Prediction of Cancer Mutation and Autonomous Drug Discovery of Anti-Cancer Therapeutic Agents, Steven Agajanian

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Allocation of Public Resources: Bringing Order to Chaos, Lance Clifner

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A Novel Correction for the Adjusted Box-Pierce Test — New Risk Factors for Emergency Department Return Visits within 72 hours for Children with Respiratory Conditions — General Pediatric Model for Understanding and Predicting Prolonged Length of Stay, Sidy Danioko

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A Computational and Experimental Examination of the FCC Incentive Auction, Logan Gantner

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Exploring the Employment Landscape for Individuals with Autism Spectrum Disorders using Supervised and Unsupervised Machine Learning, Kayleigh Hyde

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Integrated Machine Learning and Bioinformatics Approaches for Prediction of Cancer-Driving Gene Mutations, Oluyemi Odeyemi

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On Quantum Effects of Vector Potentials and Generalizations of Functional Analysis, Ismael L. Paiva

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Long Term Ground Based Precipitation Data Analysis: Spatial and Temporal Variability, Luciano Rodriguez

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Gaining Computational Insight into Psychological Data: Applications of Machine Learning with Eating Disorders and Autism Spectrum Disorder, Natalia Rosenfield

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Connecting the Dots for People with Autism: A Data-driven Approach to Designing and Evaluating a Global Filter, Viseth Sean

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Novel Statistical and Machine Learning Methods for the Forecasting and Analysis of Major League Baseball Player Performance, Christopher Watkins

Dissertations from 2019

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Contributions to Variable Selection in Complexly Sampled Case-control Models, Epidemiology of 72-hour Emergency Department Readmission, and Out-of-site Migration Rate Estimation Using Pseudo-tagged Longitudinal Data, Kyle Anderson

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Bias Reduction in Machine Learning Classifiers for Spatiotemporal Analysis of Coral Reefs using Remote Sensing Images, Justin J. Gapper

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Estimating Auction Equilibria using Individual Evolutionary Learning, Kevin James

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Employing Earth Observations and Artificial Intelligence to Address Key Global Environmental Challenges in Service of the SDGs, Wenzhao Li

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Image Restoration using Automatic Damaged Regions Detection and Machine Learning-Based Inpainting Technique, Chloe Martin-King

Theses from 2017

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Optimized Forecasting of Dominant U.S. Stock Market Equities Using Univariate and Multivariate Time Series Analysis Methods, Michael Schwartz